good examples, as they provide guidance on how to interpret results and what kind
of conclusions could not be drawn.
3.4 Technological Representativeness
TIMES inventories represent future supply chains, while most of the LCI data are
from current supply chains. Parameters such as efficiency, capacity factors or
emission factors of the same technology may therefore differ. Unless a harmonisation step is introduced, the integration could lead to inconsistencies. The level of
consistency in the different integration is difficult to evaluate because, with some
exceptions (e.g. [5, 8]) it is not well documented and rarely discussed. Studies using
EEIO data acknowledge that sectoral aggregation is an additional problem, as
sectors with different emission intensities can be grouped together, eroding data
representativeness [8]. References [3, 30] suggested learning from the experiences
linking bottom-up and general equilibrium models. Some of the concepts developed
during other integration efforts can be applied to the integration of TIMES models
and LCA databases. For example, the introduction of common measurement points
(i.e. points where both models have the same result) would be useful to formalise
the linking approach.
3.5 Dealing with Multifunctional Processes
Multifunctional processes should be handled in a way that is consistent with the
scope of the analysis and if possible, avoiding the allocation using system expansion [20]. However, except Ref. [6], studies do not specify how allocation has been
conducted when it could not be avoided (e.g. one of the outputs being outside the
system boundary).
4 Discussion and Conclusions
The TIMES-LCA integration is clearly in expansion, with several recent publications on the topic. Integration can range from a very simple level (e.g. identification
of future average or marginal technologies in a particular energy market), to more
detailed ones, capturing market and policy interactions and transformations in the
entire energy supply chain. More comprehensive approaches also imply a substantial additional effort, particularly solving the mapping of processes between the
two bottom-up models.
To date, the existing literature has focused on attributional studies of electricity
generation, although some multisector and consequential studies exist. Limiting the
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of conclusions could not be drawn.
3.4 Technological Representativeness
TIMES inventories represent future supply chains, while most of the LCI data are
from current supply chains. Parameters such as efficiency, capacity factors or
emission factors of the same technology may therefore differ. Unless a harmonisation step is introduced, the integration could lead to inconsistencies. The level of
consistency in the different integration is difficult to evaluate because, with some
exceptions (e.g. [5, 8]) it is not well documented and rarely discussed. Studies using
EEIO data acknowledge that sectoral aggregation is an additional problem, as
sectors with different emission intensities can be grouped together, eroding data
representativeness [8]. References [3, 30] suggested learning from the experiences
linking bottom-up and general equilibrium models. Some of the concepts developed
during other integration efforts can be applied to the integration of TIMES models
and LCA databases. For example, the introduction of common measurement points
(i.e. points where both models have the same result) would be useful to formalise
the linking approach.
3.5 Dealing with Multifunctional Processes
Multifunctional processes should be handled in a way that is consistent with the
scope of the analysis and if possible, avoiding the allocation using system expansion [20]. However, except Ref. [6], studies do not specify how allocation has been
conducted when it could not be avoided (e.g. one of the outputs being outside the
system boundary).
4 Discussion and Conclusions
The TIMES-LCA integration is clearly in expansion, with several recent publications on the topic. Integration can range from a very simple level (e.g. identification
of future average or marginal technologies in a particular energy market), to more
detailed ones, capturing market and policy interactions and transformations in the
entire energy supply chain. More comprehensive approaches also imply a substantial additional effort, particularly solving the mapping of processes between the
two bottom-up models.
To date, the existing literature has focused on attributional studies of electricity
generation, although some multisector and consequential studies exist. Limiting the
256
M. F. Astudillo et al.